{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "coronavirus-spark.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP5FkdfeoObN9MBkuvl1gSo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oskar-furmanczuk/coronavirus-spark-PY/blob/master/coronavirus_spark.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EI8po0LlimLl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# By projekt był kompatybilny ze standardowym środowiskiem Google Colab niezbędny jest run poniższego bloku kodu\n",
        "%%capture\n",
        "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n",
        "!wget -q https://www-us.apache.org/dist/spark/spark-3.0.0-preview2/spark-3.0.0-preview2-bin-hadoop3.2.tgz\n",
        "!tar -xvf spark-3.0.0-preview2-bin-hadoop3.2.tgz\n",
        "!pip install -q findspark\n",
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = \"/content/spark-3.0.0-preview2-bin-hadoop3.2\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNnH1Ybqj0Zr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import findspark\n",
        "findspark.init()\n",
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.master(\"local[*]\").getOrCreate()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ag7PoP8bctC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjfqUvZTqclD",
        "colab_type": "code",
        "outputId": "279b7237-5861-4cc7-96c1-9b8c7cfeab4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        }
      },
      "source": [
        "url = \"https://opendata.ecdc.europa.eu/covid19/casedistribution/csv\"\n",
        "df = pd.read_csv(url)\n",
        "print(\"\\nPRZED USUNIĘCIEM NIEPEŁNYCH WIERSZY\\n\")\n",
        "df.info()\n",
        "df.dropna(inplace=True)\n",
        "print(\"\\nPO USUNIĘCIU NIEPEŁNYCH WIERSZY\\n\")\n",
        "df.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "PRZED USUNIĘCIEM NIEPEŁNYCH WIERSZY\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 22801 entries, 0 to 22800\n",
            "Data columns (total 11 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   dateRep                  22801 non-null  object \n",
            " 1   day                      22801 non-null  int64  \n",
            " 2   month                    22801 non-null  int64  \n",
            " 3   year                     22801 non-null  int64  \n",
            " 4   cases                    22801 non-null  int64  \n",
            " 5   deaths                   22801 non-null  int64  \n",
            " 6   countriesAndTerritories  22801 non-null  object \n",
            " 7   geoId                    22712 non-null  object \n",
            " 8   countryterritoryCode     22473 non-null  object \n",
            " 9   popData2018              22455 non-null  float64\n",
            " 10  continentExp             22801 non-null  object \n",
            "dtypes: float64(1), int64(5), object(5)\n",
            "memory usage: 1.9+ MB\n",
            "\n",
            "PO USUNIĘCIU NIEPEŁNYCH WIERSZY\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 22302 entries, 0 to 22800\n",
            "Data columns (total 11 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   dateRep                  22302 non-null  object \n",
            " 1   day                      22302 non-null  int64  \n",
            " 2   month                    22302 non-null  int64  \n",
            " 3   year                     22302 non-null  int64  \n",
            " 4   cases                    22302 non-null  int64  \n",
            " 5   deaths                   22302 non-null  int64  \n",
            " 6   countriesAndTerritories  22302 non-null  object \n",
            " 7   geoId                    22302 non-null  object \n",
            " 8   countryterritoryCode     22302 non-null  object \n",
            " 9   popData2018              22302 non-null  float64\n",
            " 10  continentExp             22302 non-null  object \n",
            "dtypes: float64(1), int64(5), object(5)\n",
            "memory usage: 2.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iihN5i0guC5Y",
        "colab_type": "code",
        "outputId": "9bfda3ad-9423-4e53-cc58-433a11036b50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        }
      },
      "source": [
        "dataset = spark.createDataFrame(df)\n",
        "dataset.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+----------+---+-----+----+-----+------+-----------------------+-----+--------------------+-----------+------------+\n",
            "|   dateRep|day|month|year|cases|deaths|countriesAndTerritories|geoId|countryterritoryCode|popData2018|continentExp|\n",
            "+----------+---+-----+----+-----+------+-----------------------+-----+--------------------+-----------+------------+\n",
            "|11/06/2020| 11|    6|2020|  684|    21|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|10/06/2020| 10|    6|2020|  542|    15|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|09/06/2020|  9|    6|2020|  575|    12|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|08/06/2020|  8|    6|2020|  791|    30|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|07/06/2020|  7|    6|2020|  582|    18|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|06/06/2020|  6|    6|2020|  915|     9|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|05/06/2020|  5|    6|2020|  787|     6|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|04/06/2020|  4|    6|2020|  758|    24|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|03/06/2020|  3|    6|2020|  759|     5|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|02/06/2020|  2|    6|2020|  545|     8|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|01/06/2020|  1|    6|2020|  680|     8|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|31/05/2020| 31|    5|2020|  866|     3|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|30/05/2020| 30|    5|2020|  623|    11|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|29/05/2020| 29|    5|2020|  580|     8|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|28/05/2020| 28|    5|2020|  625|     7|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|27/05/2020| 27|    5|2020|  658|     1|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|26/05/2020| 26|    5|2020|  591|     1|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|25/05/2020| 25|    5|2020|  584|     2|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|24/05/2020| 24|    5|2020|  782|    11|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "|23/05/2020| 23|    5|2020|  540|    12|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|\n",
            "+----------+---+-----+----+-----+------+-----------------------+-----+--------------------+-----------+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZkNOjUewj6K",
        "colab_type": "code",
        "outputId": "9ba815ba-6e9b-47f6-f334-29cee5904034",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "dataset.printSchema()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "root\n",
            " |-- dateRep: string (nullable = true)\n",
            " |-- day: long (nullable = true)\n",
            " |-- month: long (nullable = true)\n",
            " |-- year: long (nullable = true)\n",
            " |-- cases: long (nullable = true)\n",
            " |-- deaths: long (nullable = true)\n",
            " |-- countriesAndTerritories: string (nullable = true)\n",
            " |-- geoId: string (nullable = true)\n",
            " |-- countryterritoryCode: string (nullable = true)\n",
            " |-- popData2018: double (nullable = true)\n",
            " |-- continentExp: string (nullable = true)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkWK4V1Qw27_",
        "colab_type": "code",
        "outputId": "f96276fa-621e-4300-d844-704a3f0c6ad4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        }
      },
      "source": [
        "import pyspark.sql.functions as F\n",
        "dataset = dataset.withColumn(\"daily_cases_per_100k_inhabitants\", dataset.cases * 100000/dataset.popData2018)\n",
        "dataset = dataset.withColumn(\"daily_deaths_per_100k_inhabitants\", dataset.deaths * 100000/dataset.popData2018)\n",
        "dataset.show()\n",
        "dataset.createOrReplaceTempView(\"dataset\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+----------+---+-----+----+-----+------+-----------------------+-----+--------------------+-----------+------------+--------------------------------+---------------------------------+\n",
            "|   dateRep|day|month|year|cases|deaths|countriesAndTerritories|geoId|countryterritoryCode|popData2018|continentExp|daily_cases_per_100k_inhabitants|daily_deaths_per_100k_inhabitants|\n",
            "+----------+---+-----+----+-----+------+-----------------------+-----+--------------------+-----------+------------+--------------------------------+---------------------------------+\n",
            "|11/06/2020| 11|    6|2020|  684|    21|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.8400755872921366|              0.05649354873265332|\n",
            "|10/06/2020| 10|    6|2020|  542|    15|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.4580715910999096|              0.04035253480903808|\n",
            "|09/06/2020|  9|    6|2020|  575|    12|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.5468471676797932|              0.03228202784723047|\n",
            "|08/06/2020|  8|    6|2020|  791|    30|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              2.1279236689299417|              0.08070506961807616|\n",
            "|07/06/2020|  7|    6|2020|  582|    18|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.5656783505906777|               0.0484230417708457|\n",
            "|06/06/2020|  6|    6|2020|  915|     9|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|               2.461504623351323|              0.02421152088542285|\n",
            "|05/06/2020|  5|    6|2020|  787|     6|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|               2.117162992980865|             0.016141013923615236|\n",
            "|04/06/2020|  4|    6|2020|  758|    24|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|               2.039148092350058|              0.06456405569446094|\n",
            "|03/06/2020|  3|    6|2020|  759|     5|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|               2.041838261337327|             0.013450844936346028|\n",
            "|02/06/2020|  2|    6|2020|  545|     8|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|               1.466142098061717|             0.021521351898153646|\n",
            "|01/06/2020|  1|    6|2020|  680|     8|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.8293149113430598|             0.021521351898153646|\n",
            "|31/05/2020| 31|    5|2020|  866|     3|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|               2.329686342975132|             0.008070506961807618|\n",
            "|30/05/2020| 30|    5|2020|  623|    11|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.6759752790687152|             0.029591858859961264|\n",
            "|29/05/2020| 29|    5|2020|  580|     8|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.5602980126161392|             0.021521351898153646|\n",
            "|28/05/2020| 28|    5|2020|  625|     7|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.6813556170432535|              0.01883118291088444|\n",
            "|27/05/2020| 27|    5|2020|  658|     1|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.7701311936231374|             0.002690168987269206|\n",
            "|26/05/2020| 26|    5|2020|  591|     1|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              1.5898898714761005|             0.002690168987269206|\n",
            "|25/05/2020| 25|    5|2020|  584|     2|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|               1.571058688565216|             0.005380337974538412|\n",
            "|24/05/2020| 24|    5|2020|  782|    11|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|              2.1037121480445187|             0.029591858859961264|\n",
            "|23/05/2020| 23|    5|2020|  540|    12|            Afghanistan|   AF|                 AFG|3.7172386E7|        Asia|               1.452691253125371|              0.03228202784723047|\n",
            "+----------+---+-----+----+-----+------+-----------------------+-----+--------------------+-----------+------------+--------------------------------+---------------------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5ezaMmMtMGK",
        "colab_type": "code",
        "outputId": "6847d933-2545-4f26-88ca-a6ba48e7a351",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "dataset_PL = dataset.filter(dataset.geoId==\"PL\")\n",
        "dataset_PL = dataset_PL.orderBy(dataset_PL.year,dataset_PL.month, dataset_PL.day )\n",
        "df_PL = dataset_PL.toPandas()\n",
        "df_PL.cases.cumsum().plot(title = \"Koronawirus w polsce\", legend=True)\n",
        "df_PL.deaths.cumsum().plot(legend=True)\n",
        "plt.xlabel('dzień epidemi')\n",
        "plt.ylabel('ilość przypadków')\n",
        "plt.show()\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m59GermN8nd5",
        "colab_type": "code",
        "outputId": "b21b927d-1906-4c8d-bce2-04592a7f039d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        }
      },
      "source": [
        "tab1 = spark.sql(\n",
        "    \"SELECT countriesAndTerritories, sum(cases) as cases_per_country, sum(deaths) as deaths_per_country\\\n",
        "     FROM dataset \\\n",
        "     GROUP BY countriesAndTerritories \\\n",
        "     ORDER BY cases_per_country DESC\" \n",
        "    )\n",
        "tab1.createOrReplaceTempView(\"tab1\")\n",
        "tab1.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----------------------+-----------------+------------------+\n",
            "|countriesAndTerritories|cases_per_country|deaths_per_country|\n",
            "+-----------------------+-----------------+------------------+\n",
            "|   United_States_of_...|          2000464|            112924|\n",
            "|                 Brazil|           772416|             39680|\n",
            "|                 Russia|           493657|              6358|\n",
            "|         United_Kingdom|           290143|             41128|\n",
            "|                  India|           286579|              8102|\n",
            "|                  Spain|           242280|             27136|\n",
            "|                  Italy|           235763|             34114|\n",
            "|                   Peru|           208823|              5903|\n",
            "|                Germany|           185416|              8755|\n",
            "|                   Iran|           177938|              8506|\n",
            "|                 Turkey|           173036|              4746|\n",
            "|                 France|           155136|             29319|\n",
            "|                  Chile|           148496|              2475|\n",
            "|                 Mexico|           129184|             15357|\n",
            "|               Pakistan|           119536|              2356|\n",
            "|           Saudi_Arabia|           112288|               819|\n",
            "|                 Canada|            97114|              7960|\n",
            "|                  China|            84209|              4638|\n",
            "|             Bangladesh|            74865|              1012|\n",
            "|                  Qatar|            73595|                66|\n",
            "+-----------------------+-----------------+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYR1mJUKGhtL",
        "colab_type": "code",
        "outputId": "4927709b-a5dd-4c87-ae82-66067595e847",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(\"\\nTABELA PRZEDSTAIWAJĄCA ILOŚĆ ZACHOROWAŃ PER KRAJ NA 1OO 000 MIESZKAŃCÓW\\n\")\n",
        "tab2 = spark.sql(\n",
        "    \"SELECT DISTINCT dataset.countriesAndTerritories, dataset.geoId, (cases_per_country/dataset.popData2018 * 100000) AS cases_per_100k_inhabitants \\\n",
        "    FROM tab1 INNER JOIN dataset ON tab1.countriesAndTerritories = dataset.countriesAndTerritories \\\n",
        "    ORDER BY cases_per_100k_inhabitants DESC\"\n",
        "    )\n",
        "tab2.show()\n",
        "\n",
        "print(\"\\nTABELA PRZEDSTAIWAJĄCA ILOŚĆ ZGONÓW PER KRAJ NA 1OO 000 MIESZKAŃCÓW\\n\")\n",
        "tab3 = spark.sql(\n",
        "    \"SELECT DISTINCT dataset.countriesAndTerritories, dataset.geoId, (deaths_per_country/dataset.popData2018 * 100000) AS deaths_per_100k_inhabitants \\\n",
        "    FROM tab1 INNER JOIN dataset ON tab1.countriesAndTerritories = dataset.countriesAndTerritories \\\n",
        "    ORDER BY deaths_per_100k_inhabitants DESC\"\n",
        "    )\n",
        "tab3.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "TABELA PRZEDSTAIWAJĄCA ILOŚĆ ZACHOROWAŃ PER KRAJ NA 1OO 000 MIESZKAŃCÓW\n",
            "\n",
            "+-----------------------+-----+--------------------------+\n",
            "|countriesAndTerritories|geoId|cases_per_100k_inhabitants|\n",
            "+-----------------------+-----+--------------------------+\n",
            "|                  Qatar|   QA|        2645.7061693359797|\n",
            "|             San_Marino|   SM|        2045.2863696906913|\n",
            "|               Holy_See|   VA|                    1200.0|\n",
            "|                Andorra|   AD|        1106.4072929382125|\n",
            "|                Bahrain|   BH|        1032.2159701651353|\n",
            "|                 Kuwait|   KW|          817.512059166961|\n",
            "|                  Chile|   CL|         792.8599040213228|\n",
            "|              Singapore|   SG|          691.031015082264|\n",
            "|             Luxembourg|   LU|         666.2520074770292|\n",
            "|                   Peru|   PE|         652.7910495949016|\n",
            "|   United_States_of_...|   US|         611.4496102322946|\n",
            "|                Belarus|   BY|         538.3650175122025|\n",
            "|              Gibraltar|   GI|         521.9763924313423|\n",
            "|                Belgium|   BE|         521.5255240994888|\n",
            "|                Ireland|   IE|          519.851010795083|\n",
            "|                  Spain|   ES|         518.5371576240597|\n",
            "|                Iceland|   IS|        511.06699021986907|\n",
            "|                Armenia|   AM|         477.7801567598625|\n",
            "|                 Sweden|   SE|        459.71909546875116|\n",
            "|               Djibouti|   DJ|         456.0338714387019|\n",
            "+-----------------------+-----+--------------------------+\n",
            "only showing top 20 rows\n",
            "\n",
            "\n",
            "TABELA PRZEDSTAIWAJĄCA ILOŚĆ ZGONÓW PER KRAJ NA 1OO 000 MIESZKAŃCÓW\n",
            "\n",
            "+-----------------------+-----+---------------------------+\n",
            "|countriesAndTerritories|geoId|deaths_per_100k_inhabitants|\n",
            "+-----------------------+-----+---------------------------+\n",
            "|             San_Marino|   SM|         124.31552464111293|\n",
            "|                Belgium|   BE|          84.30172189484426|\n",
            "|                Andorra|   AD|          66.22860556320288|\n",
            "|         United_Kingdom|   UK|          61.85685687424554|\n",
            "|                  Spain|   ES|         58.077531406993906|\n",
            "|                  Italy|   IT|          56.45089481221175|\n",
            "|                 Sweden|   SE|          47.08747517350925|\n",
            "|                 France|   FR|          43.76803440368438|\n",
            "|           Sint_Maarten|   SX|          36.15677577978113|\n",
            "|            Netherlands|   NL|         35.064674360195916|\n",
            "|                Ireland|   IE|          34.92320808916276|\n",
            "|   United_States_of_...|   US|          34.51566025975556|\n",
            "|            Isle_of_Man|   IM|         28.545262081187484|\n",
            "|                 Jersey|   JE|          28.08988764044944|\n",
            "|                Ecuador|   EC|         21.774304997255676|\n",
            "|                 Canada|   CA|         21.479346259366455|\n",
            "|               Guernsey|   GG|         20.626408149017866|\n",
            "|            Switzerland|   CH|         19.655862713309848|\n",
            "|                 Brazil|   BR|         18.943107056153178|\n",
            "|                   Peru|   PE|          18.45307061846015|\n",
            "+-----------------------+-----+---------------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QI6tyhhmHuKv",
        "colab_type": "code",
        "outputId": "7cc69b13-db8f-432d-a796-78470afb30b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "df1 = tab2.limit(15).toPandas()\n",
        "df2 = tab3.limit(15).toPandas()\n",
        "plt.subplot(2,1,1)\n",
        "plt.bar(df1.geoId, df1.cases_per_100k_inhabitants)\n",
        "plt.title(\"WYKRES PRZEDSTAIWAJĄCY ILOŚĆ ZACHOROWAŃ PER KRAJ NA 1OO 000 MIESZKAŃCÓW \")\n",
        "plt.show()\n",
        "plt.subplot(2,1,2)\n",
        "plt.bar(df2.geoId, df2.deaths_per_100k_inhabitants)\n",
        "plt.title(\"WYKRES PRZEDSTAIWAJĄCY ILOŚĆ ZGONY PER KRAJ NA 1OO 000 MIESZKAŃCÓW \")\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-Gfq5fgQZl9",
        "colab_type": "code",
        "outputId": "108062c0-9029-46b9-897b-30f40dbf9d1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        }
      },
      "source": [
        "### ANALIZA PORÓWNAWCZA - PORÓWNANIE ILOŚCI ZGONÓW W POLSCE I SZWAJCARII\n",
        "\n",
        "tab_pl = spark.sql(\n",
        "    \"SELECT * \\\n",
        "     FROM dataset \\\n",
        "     WHERE geoId LIKE 'PL' \\\n",
        "     ORDER BY YEAR, MONTH, DAY \"\n",
        "    )\n",
        "tab_pl.createOrReplaceTempView(\"tab_pl\")\n",
        "\n",
        "tab_ch = spark.sql(\n",
        "    \"SELECT * \\\n",
        "     FROM dataset \\\n",
        "     WHERE geoId LIKE 'CH' \\\n",
        "     ORDER BY YEAR, MONTH, DAY \"\n",
        "    )\n",
        "tab_ch.createOrReplaceTempView(\"tab_ch\")\n",
        "\n",
        "tab_join = spark.sql(\n",
        "    \"SELECT DISTINCT tab_pl.dateRep,tab_pl.year, tab_pl.month, tab_pl.day, tab_ch.daily_deaths_per_100k_inhabitants AS daily_deaths_per_100k_inhabitants_in_Switzerland, tab_pl.daily_deaths_per_100k_inhabitants AS daily_deaths_per_100k_inhabitants_in_Poland \\\n",
        "     FROM tab_pl INNER JOIN tab_ch ON tab_pl.dateRep = tab_ch.dateRep \\\n",
        "     ORDER BY 2,3,4 \"\n",
        "    )\n",
        "tab_join.createOrReplaceTempView(\"tab_join\")\n",
        "tab_join.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+----------+----+-----+---+------------------------------------------------+-------------------------------------------+\n",
            "|   dateRep|year|month|day|daily_deaths_per_100k_inhabitants_in_Switzerland|daily_deaths_per_100k_inhabitants_in_Poland|\n",
            "+----------+----+-----+---+------------------------------------------------+-------------------------------------------+\n",
            "|04/03/2020|2020|    3|  4|                                             0.0|                                        0.0|\n",
            "|07/03/2020|2020|    3|  7|                                             0.0|                                        0.0|\n",
            "|08/03/2020|2020|    3|  8|                                             0.0|                                        0.0|\n",
            "|09/03/2020|2020|    3|  9|                            0.011741853472706003|                                        0.0|\n",
            "|10/03/2020|2020|    3| 10|                                             0.0|                                        0.0|\n",
            "|11/03/2020|2020|    3| 11|                            0.011741853472706003|                                        0.0|\n",
            "|12/03/2020|2020|    3| 12|                            0.011741853472706003|                                        0.0|\n",
            "|13/03/2020|2020|    3| 13|                                             0.0|                       0.002633065382067...|\n",
            "|14/03/2020|2020|    3| 14|                             0.03522556041811801|                       0.002633065382067...|\n",
            "|15/03/2020|2020|    3| 15|                             0.04696741389082401|                       0.002633065382067...|\n",
            "|16/03/2020|2020|    3| 16|                            0.023483706945412006|                                        0.0|\n",
            "|17/03/2020|2020|    3| 17|                            0.011741853472706003|                       0.002633065382067...|\n",
            "|18/03/2020|2020|    3| 18|                            0.058709267363530014|                       0.002633065382067...|\n",
            "|19/03/2020|2020|    3| 19|                            0.023483706945412006|                                        0.0|\n",
            "|20/03/2020|2020|    3| 20|                             0.14090224167247203|                                        0.0|\n",
            "|21/03/2020|2020|    3| 21|                             0.11741853472706003|                                        0.0|\n",
            "|22/03/2020|2020|    3| 22|                             0.15264409514517804|                                        0.0|\n",
            "|23/03/2020|2020|    3| 23|                             0.04696741389082401|                       0.005266130764135...|\n",
            "|24/03/2020|2020|    3| 24|                             0.07045112083623602|                       0.002633065382067...|\n",
            "|25/03/2020|2020|    3| 25|                             0.23483706945412006|                       0.005266130764135...|\n",
            "+----------+----+-----+---+------------------------------------------------+-------------------------------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmmOlQU79fEZ",
        "colab_type": "code",
        "outputId": "e299e738-8ad9-4e57-a176-4ac5ff1a4fb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "source": [
        "df_join = tab_join.toPandas()\n",
        "df_join.set_index('dateRep')\n",
        "df_join.daily_deaths_per_100k_inhabitants_in_Switzerland.plot(title = \"Dzienne zgony na 100k mieszańców\", legend=True)\n",
        "plt.xlabel('dzień epidemi')\n",
        "plt.ylabel('ilość zmarłych')\n",
        "df_join.daily_deaths_per_100k_inhabitants_in_Poland.plot(legend=True)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "df_join = tab_join.toPandas()\n",
        "df_join.daily_deaths_per_100k_inhabitants_in_Switzerland.rolling(window = 10).mean().plot(title = \"Dzienne zgony na 100k mieszańców - średnia ruchoma (N=10)\", legend=True)\n",
        "plt.xlabel('dzień epidemi')\n",
        "plt.ylabel('ilość zmarłych')\n",
        "df_join.daily_deaths_per_100k_inhabitants_in_Poland.rolling(window = 10).mean().plot(legend=True)\n",
        "\n",
        "plt.show()\n",
        "print(\"- Jak można wnioskować z wykresu Szajcarzy dużo dotkliwiej odczuli skutki epidemi. \\\n",
        "\\n- W ostatnich 40 dniach epidemii można zauważyć stosunkowy spadek śmiertelnści na terenie Szwajcarii.   \\\n",
        "\\n- W polsce wskażnik ten jest  stabilny i utrzymuję się na stosunkowo niskim poziomie. \\\n",
        "\\n- Można również zauważyć że w ciągu ostatnich 30 dni wkaźnik dla Polski i Szwajcarii utrzymuję się na zbliżonym poziomie.\" )"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "- Jak można wnioskować z wykresu Szajcarzy dużo dotkliwiej odczuli skutki epidemi. \n",
            "- W ostatnich 40 dniach epidemii można zauważyć stosunkowy spadek śmiertelnści na terenie Szwajcarii.   \n",
            "- W polsce wskażnik ten jest  stabilny i utrzymuję się na stosunkowo niskim poziomie. \n",
            "- Można również zauważyć że w ciągu ostatnich 30 dni wkaźnik dla Polski i Szwajcarii utrzymuję się na zbliżonym poziomie.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SbYnL0QJN8d",
        "colab_type": "code",
        "outputId": "f3986650-e371-4a2b-cca9-832c72a88bb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 22302 entries, 0 to 22800\n",
            "Data columns (total 11 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   dateRep                  22302 non-null  object \n",
            " 1   day                      22302 non-null  int64  \n",
            " 2   month                    22302 non-null  int64  \n",
            " 3   year                     22302 non-null  int64  \n",
            " 4   cases                    22302 non-null  int64  \n",
            " 5   deaths                   22302 non-null  int64  \n",
            " 6   countriesAndTerritories  22302 non-null  object \n",
            " 7   geoId                    22302 non-null  object \n",
            " 8   countryterritoryCode     22302 non-null  object \n",
            " 9   popData2018              22302 non-null  float64\n",
            " 10  continentExp             22302 non-null  object \n",
            "dtypes: float64(1), int64(5), object(5)\n",
            "memory usage: 2.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOlqL2D1JUGA",
        "colab_type": "code",
        "outputId": "4e6983e0-7ba0-4281-e0e9-bf63a11db4f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        }
      },
      "source": [
        "### ANALIZA PREDYKCYJNA - PROGNOZA SKUMULOWANEJ ILOŚĆ ZACHOROWAŃ NA TERENIE STANÓW ZJEDNOCZONYCH\n",
        "\n",
        "tab_us = spark.sql(\n",
        "    \"SELECT * \\\n",
        "     FROM dataset \\\n",
        "     WHERE geoId LIKE 'US' \\\n",
        "     ORDER BY YEAR ASC, MONTH ASC, DAY ASC \"\n",
        "    )\n",
        "tab_us.createOrReplaceTempView(\"tab_us\")\n",
        "tab_us.show(5)\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+----------+---+-----+----+-----+------+-----------------------+-----+--------------------+------------+------------+--------------------------------+---------------------------------+\n",
            "|   dateRep|day|month|year|cases|deaths|countriesAndTerritories|geoId|countryterritoryCode| popData2018|continentExp|daily_cases_per_100k_inhabitants|daily_deaths_per_100k_inhabitants|\n",
            "+----------+---+-----+----+-----+------+-----------------------+-----+--------------------+------------+------------+--------------------------------+---------------------------------+\n",
            "|31/12/2019| 31|   12|2019|    0|     0|   United_States_of_...|   US|                 USA|3.27167434E8|     America|                             0.0|                              0.0|\n",
            "|01/01/2020|  1|    1|2020|    0|     0|   United_States_of_...|   US|                 USA|3.27167434E8|     America|                             0.0|                              0.0|\n",
            "|02/01/2020|  2|    1|2020|    0|     0|   United_States_of_...|   US|                 USA|3.27167434E8|     America|                             0.0|                              0.0|\n",
            "|03/01/2020|  3|    1|2020|    0|     0|   United_States_of_...|   US|                 USA|3.27167434E8|     America|                             0.0|                              0.0|\n",
            "|04/01/2020|  4|    1|2020|    0|     0|   United_States_of_...|   US|                 USA|3.27167434E8|     America|                             0.0|                              0.0|\n",
            "+----------+---+-----+----+-----+------+-----------------------+-----+--------------------+------------+------------+--------------------------------+---------------------------------+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mWLTzRA5TJq",
        "colab_type": "code",
        "outputId": "90e15229-3aea-4182-aca0-bd70e10bc1f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import datetime\n",
        "from datetime import date\n",
        "\n",
        "today = date.today()\n",
        "today_l = str(today).split(\"-\")\n",
        "today_year = int(today_l[0])\n",
        "today_month = int(today_l[1])\n",
        "today_day = int(today_l[2])\n",
        "\n",
        "delta = datetime.timedelta(days=30)\n",
        "\n",
        "today + delta"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.date(2020, 7, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vA-zSmwWW9bn",
        "colab_type": "code",
        "outputId": "156e19f8-f59b-467e-fbaa-8516ae707c5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "from pyspark.sql import Window\n",
        "from pyspark.sql.functions import monotonically_increasing_id\n",
        "\n",
        "tab_us = tab_us.withColumn('cumulative_cases', F.sum('cases').over(Window.rowsBetween(Window.unboundedPreceding, 0)))\n",
        "tab_us = tab_us.filter(tab_us.cumulative_cases > 0)\n",
        "\n",
        "tab_us = tab_us.withColumn(\"nr_of_the_pandemic_day\", monotonically_increasing_id())\n",
        "tab_us_model = tab_us.select(tab_us.nr_of_the_pandemic_day, tab_us.cumulative_cases)\n",
        "nr_of_tomorrow_pandemic_day = tab_us_model.count()\n",
        "tab_us_model = tab_us_model.orderBy(tab_us_model.cumulative_cases, ascending = False).limit(10)\n",
        "tab_us_model.show(5)\n",
        "print(nr_of_tomorrow_pandemic_day)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+----------------------+----------------+\n",
            "|nr_of_the_pandemic_day|cumulative_cases|\n",
            "+----------------------+----------------+\n",
            "|                   142|         2000464|\n",
            "|                   141|         1979850|\n",
            "|                   140|         1961185|\n",
            "|                   139|         1942363|\n",
            "|                   138|         1920061|\n",
            "+----------------------+----------------+\n",
            "only showing top 5 rows\n",
            "\n",
            "143\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pmHVoF6_pzQX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pyspark.ml.feature import VectorAssembler\n",
        "vectorAssembler = VectorAssembler(inputCols = ['nr_of_the_pandemic_day'], outputCol = 'features')\n",
        "tab_us_model_trans = vectorAssembler.transform(tab_us_model)\n",
        "tab_us_model_trans = tab_us_model_trans.select(['features', 'cumulative_cases'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8K6k3PnBszL",
        "colab_type": "code",
        "outputId": "eefbad8a-8824-410a-83d2-d4c995ac57a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from pyspark.ml.regression import LinearRegression\n",
        "lr = LinearRegression(featuresCol = 'features', labelCol='cumulative_cases')\n",
        "lr_model = lr.fit(tab_us_model_trans)\n",
        "print(\"Coefficients: \" + str(lr_model.coefficients))\n",
        "print(\"Intercept: \" + str(lr_model.intercept))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: [21324.503030306532]\n",
            "Intercept: -1025215.2666671481\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPnFj01LIZxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "next_d = 5\n",
        "df_len = nr_of_tomorrow_pandemic_day + next_d\n",
        "freq = 1\n",
        "test_set = spark.range(nr_of_tomorrow_pandemic_day, df_len, freq).toDF(\"next_{}_days\".format(next_d))\n",
        "\n",
        "vectorAssembler = VectorAssembler(inputCols = [\"next_{}_days\".format(next_d)], outputCol = 'features')\n",
        "tab_us_forecast_trans = vectorAssembler.transform(test_set)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlVccgLHhrS_",
        "colab_type": "code",
        "outputId": "fd21375a-ef4d-4853-83a1-b6b9dbd397f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "source": [
        "lr_predictions = lr_model.transform(tab_us_forecast_trans)\n",
        "results = lr_predictions.withColumn(\"forecast\", F.round(lr_predictions.prediction))\n",
        "results = results.select([(\"next_{}_days\".format(next_d)), results.forecast])\n",
        "results.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----------+---------+\n",
            "|next_5_days| forecast|\n",
            "+-----------+---------+\n",
            "|        143|2024189.0|\n",
            "|        144|2045513.0|\n",
            "|        145|2066838.0|\n",
            "|        146|2088162.0|\n",
            "|        147|2109487.0|\n",
            "+-----------+---------+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4MKF6kDy3WJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_future = results.toPandas()\n",
        "df_past = tab_us.toPandas()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMzdi1pfBnjc",
        "colab_type": "code",
        "outputId": "2ac862ca-eca6-466f-d76a-77043d09abd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        }
      },
      "source": [
        "plt.plot(df_future[\"next_{}_days\".format(next_d)], list(df_future.forecast),'--b', label = \"predykcja na następne {} dni\".format(next_d))\n",
        "plt.plot(list(df_past.nr_of_the_pandemic_day), list(df_past.cumulative_cases),'--r', label = \"dane historyczne\")\n",
        "plt.legend(loc='upper left', frameon=False)\n",
        "plt.xlabel(\"dzień pandemii w USA\")\n",
        "plt.ylabel(\"skumulowana liczba przypadków (M)\")\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gj57WaytBseX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yesterday = list(df_past.cumulative_cases)[-2]\n",
        "today = list(df_past.cumulative_cases)[-1]\n",
        "tomorrow = list(df_future.forecast)[0]\n",
        "the_day_after_tomorrow = list(df_future.forecast)[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfGy1oDVJHeC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "b4bcb66d-b9d6-4001-ddb8-4afea1303604"
      },
      "source": [
        "print(\"Informacje na temat rozwoju pandemii w USA dla najbliższych 2 dni.\\n\")\n",
        "pd.DataFrame({\"dzień\":[\"wczoraj\", \"dzisiaj\", \"jutro\", \"pojutrze\"],\"skumulowana_ilość_przypadków\":[yesterday, today, tomorrow, the_day_after_tomorrow]} )\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Informacje na temat rozwoju pandemii w USA dla najbliższych 2 dni.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dzień</th>\n",
              "      <th>skumulowana_ilość_przypadków</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>wczoraj</td>\n",
              "      <td>1979850.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>dzisiaj</td>\n",
              "      <td>2000464.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>jutro</td>\n",
              "      <td>2024189.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>pojutrze</td>\n",
              "      <td>2045513.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      dzień  skumulowana_ilość_przypadków\n",
              "0   wczoraj                     1979850.0\n",
              "1   dzisiaj                     2000464.0\n",
              "2     jutro                     2024189.0\n",
              "3  pojutrze                     2045513.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}